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Skyline Queries Against Mobile Lightweight Devices in MANETs Zhiyong Huang 1 Christian S. Jensen Skyline Queries Against Mobile Lightweight Devices in MANETs Zhiyong Huang 1 Christian S. Jensen 2 Hua Lu 1 Beng Chin Ooi 1 1 2 National University of Singapore, Singapore Aalborg University, Denmark

Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Mobile Devices • Experimental Studies • Conclusion

Introduction • Skyline query – Operator based on dominance • MANET – Self-organizing, wireless Introduction • Skyline query – Operator based on dominance • MANET – Self-organizing, wireless mobile ad-hoc networks – Physical environment of this work • Lightweight devices

Skyline Queries in MANETs • Assumptions – Each resource-constrained device holds a portion of Skyline Queries in MANETs • Assumptions – Each resource-constrained device holds a portion of the entire dataset – Devices communicate through MANET – A mobile user is only interested in data of a limited geographical area, though the query involves data stored on multiple mobile devices

Example • M 1 to M 4 hold different hotel relations • M 2 Example • M 1 to M 4 hold different hotel relations • M 2 is interested in cheap and good hotels within the circle area M 1 M 2 M 3 M 4

Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Mobile Devices • Experimental Studies • Conclusion and Future Work

Problem Setting • MANET of m mobile devices – {M 1, M 2, …, Problem Setting • MANET of m mobile devices – {M 1, M 2, …, Mm} • Local relation Ri on each device Mi – • Skyline issued by a device Morg – • id: network id of query originator Morg • pos: position of Morg • d: distance (from pos) of interest

Technical Challenges • Slow and unreliable wireless channels compared to wired connections – To Technical Challenges • Slow and unreliable wireless channels compared to wired connections – To reduce data transferred between devices • Resource-constrained devices – Storage and processing saving techniques on mobile devices

Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Mobile Devices • Experimental Studies • Conclusion

Straightforward Strategy • Query originator Morg – Executing a local skyline query: SKorg – Straightforward Strategy • Query originator Morg – Executing a local skyline query: SKorg – Sends query to other mobile devices – Merges results when receiving them • A mobile device Mi – Executing a local skyline query too – Sends result SKi back to Morg – Instead of sending whole Ri

Discussion • Final skyline result: SK USKi U| • SK≠USKi, SK • FSK = Discussion • Final skyline result: SK USKi U| • SK≠USKi, SK • FSK = USKi–SK • FSK contains all those tuples that are not in SK but sent between devices • Identify SKi–SK on device Mi – Inspiration of semi-join

Filtering Strategy • Any tuple tpi in SKi–SK is dominated by some tuple(s) tpj Filtering Strategy • Any tuple tpi in SKi–SK is dominated by some tuple(s) tpj in SK • Where to find such tpjs? – Pick from Morg’s local result – Send as query – Mi filters out tuples using tpj • Which one to pick? – Dominating region

Dominating Region • The ability of tpj to dominate others – Tuple value <pj Dominating Region • The ability of tpj to dominate others – Tuple value – Data space boundaries • Volume of dominating region – VDRj=∏k(bk-pjk) • Choose from SKorg tpflt with max VDRj p 2 Max corner of data space b 2 Dominating Region – Indep. distribution p j 2 0 tp j 1 p 1 b 1

Dominating Ability • Two hotel relations – Price range (20. . 200) – Smaller Dominating Ability • Two hotel relations – Price range (20. . 200) – Smaller rating means better (1. . 10) Hotel Price Rating Hotel h 11 20 7 h 21 60 3 (200 -60)*(10 -3)=980 h 12 40 5 h 22 80 2 (200 -80)*(10 -2)=960 h 13 80 7 h 23 120 1 (200 -120)*(10 -1)=720 h 14 80 4 h 24 140 2 − h 15 100 7 h 25 100 4 − h 16 100 3 Relation R 1 Price Rating Relation R 2 (Morg) VDR

Estimated Dominating Region • Over-estimation – VDRj=∏k(maxk-pjk) – maxk: pre-specified larger value • Under-estimation Estimated Dominating Region • Over-estimation – VDRj=∏k(maxk-pjk) – maxk: pre-specified larger value • Under-estimation – VDRj=∏k(hk-pjk) – hk: local maximum

Dynamic Filtering Tuples • Three hotel relations – M 4 -> M 3 -> Dynamic Filtering Tuples • Three hotel relations – M 4 -> M 3 -> M 1 VDR 31=980 VDR 41=960 Hotel Price Rating Hotel h 11 20 7 h 31 60 3 h 41 80 2 h 12 40 5 h 32 80 5 h 42 120 1 h 13 80 7 h 33 120 4 h 43 140 2 h 14 80 4 h 15 100 7 h 16 100 3 Relation R 1 Relation R 3 Price Rating Relation R 4 (Morg)

Query Log Mechanism • To avoid the same query more than once on any Query Log Mechanism • To avoid the same query more than once on any device Mi • Add a tag cnt to query issued by Morg – • Mi records/checks/updates – Processes and forwards only cntlog

Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Mobile Devices • Experimental Studies • Conclusion

Dataset Storage • Goals – Space efficient – Local processing efficient • Operations – Dataset Storage • Goals – Space efficient – Local processing efficient • Operations – Spatial extent check • Distinct coordinates – Attribute value comparison • Floats • Duplicates

Hybrid Storage Model • Spatial coordinates – Real values – MBRi(xmax, ymax, xmin, ymin) Hybrid Storage Model • Spatial coordinates – Real values – MBRi(xmax, ymax, xmin, ymin) • Attribute values – Ascending domains Relation R – IDs x y 1. 331 103. 67 – Sort p 1 i p 1 … pn Sorted domains p 1 pn 0 … 2 v 0 1. 329 103. 59 1 … 0 v 1 1. 412 103. 77 2 … j … … … … vk vj 1. 429 103. 95 k … 3 … v 1

Local Skyline Computing • Sptial check – mindist(posorg, MBRi) > d • Skyline computing Local Skyline Computing • Sptial check – mindist(posorg, MBRi) > d • Skyline computing – Comparison of IDs instead of true values of float type – p 2 to pn only • Update filtering tuple if necessary – Choose the one with larger VDR value

Assembly on Query Originator • When Morg receives SKi from others – Duplication elimination Assembly on Query Originator • When Morg receives SKi from others – Duplication elimination – False positive removing • A simple nested loop is enough – Comparing coordinates • Identify duplicates – Comparing attribute values • Identify false positive reports from both SKorg and SKi

Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Mobile Devices • Experimental Studies • Conclusion

Experiment Parameters Number of mobile device Cardinality of global reln Cardinality of local reln Experiment Parameters Number of mobile device Cardinality of global reln Cardinality of local reln Local storage model 32, 42, …, 102 100 K, 200 K, …, 1000 K 10 K, 20 K, …, 100 K Flat, Hybrid Number of non-spatial attr Non-spatial attri range Spatial extent of global reln Attribute distribution Query distance of interest 2, 3, 4, 5 [0. 0, 9. 9], [0, 1000] 1000 X 1000 Indep. , Anti-Correl. 100, 250, 500

Studies on Local Optimization • HP i. PAQ h 6365 pocket PC – MS Studies on Local Optimization • HP i. PAQ h 6365 pocket PC – MS Windows Mobile 2003 – 200 MHz TI OMAP 1510 processor – 64 MB SDRAM (55 MB user accessible) • Super. Waba – Java-based open-source platform for PDA and smartphone applications – www. superwaba. org

Time vs Local Cardinality • Flat Storage vs Hybrid Storage • Anti-Correlated vs Independent Time vs Local Cardinality • Flat Storage vs Hybrid Storage • Anti-Correlated vs Independent • HS incurs less processing cost

Time vs Local Dimensionality • Average of costs on both distributions – Coz they Time vs Local Dimensionality • Average of costs on both distributions – Coz they are close to each other • HS still performs better

Performance in Simulation • Simulated MANET – Ji. ST-SWANS • A Jave based MANET Performance in Simulation • Simulated MANET – Ji. ST-SWANS • A Jave based MANET simulator • http: //jist. ece. cornell. edu/ – Pentium IV desktop PC • MS Windows XP • 2. 99 GHz CPU • 1 GB memory

Settings • Device setting – Data partitioned and allocated to devices using a grid Settings • Device setting – Data partitioned and allocated to devices using a grid of m 1/2 by m 1/2 – 1 -5 queries per device • MANET settings – Total simulation time: 2 hours – Speed range: 2 unit/s – 10 unit/s – Holding time: 120 seconds – Wireless routing protocol: AODV

Data Reduction Efficiency • Data Reduction Rate – – SKi’ is the local skyline Data Reduction Efficiency • Data Reduction Rate – – SKi’ is the local skyline after filtering • Pre-tests in static setting – Forwarding query out recursively – Findings • No significant difference between exact VDR and estimated VDRs • Dynamic filtering is more powerful

Data Reduction Rate Data Reduction Rate

Response Time - BF • Breadth-First query forwarding – Parallel • Time receiving answers Response Time - BF • Breadth-First query forwarding – Parallel • Time receiving answers from 80% other devices – Cannot ensure all devices are always reachable and available in MANETs M 2 M 1 M 3 Morg M 5 M 4 Query message Result message

Response Time - DF • Depth-First forwarding – Serialized • Query ends when originator Response Time - DF • Depth-First forwarding – Serialized • Query ends when originator finds all neighbors have processed the query M 2 M 3 M 1 M 4 M 5 Morg Query message Result message

Response Time Response Time

Query Message Count • Only mobile device number affects the query message count obviously Query Message Count • Only mobile device number affects the query message count obviously • Better performance of BF is not free

Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Outline • Introduction • Problem Definition • Skyline Queries in MANETs • Optimizations on Mobile Devices • Experimental Studies • Conclusion

Conclusion • Problem setting – MANET of lightweight devices – Skyline queries with spatial Conclusion • Problem setting – MANET of lightweight devices – Skyline queries with spatial constraints • Solution highlights – Filtering based distributed query processing strategy to reduce communication cost – Specialized local storage and algorithm to speed up local processing – Experimentally verified performance

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